Automated classification or description of patterns is a challenging task. Standard pattern recognition techniques typically include a transducer converting images, sounds or other physical inputs into signal data, segmentation isolating desired data within the signal data, and feature extraction measuring properties of the signal data useful for classification and employed to assign the signal data to a particular category. Optional post-processing may take other factors into account, such as the cost of an erroneous classification, and decides upon appropriate action. Some systems employ feedback to improve overall classification accuracy.
The most widely utilized pattern recognition techniques are based on the Bayes Theorem, a fundamental theory of inverse probability stated mathematically in equation (1) below:
                              p          ⁡                      (                                          A                j                            ❘              B                        )                          =                                            p              ⁡                              (                                  B                  ❘                                      A                    j                                                  )                                      ·                          p              ⁡                              (                                  A                  j                                )                                                                        ∑              j                        ⁢                                                  ⁢                                          p                ⁡                                  (                                      B                    ❘                                          A                      j                                                        )                                            ·                              p                ⁡                                  (                                      A                    j                                    )                                                                                        (        1        )            The Bayes Theorem postulates that, for a given event B that has occurred, the probability that event B was due to a cause Aj is equal to the probability that cause Aj should produce the event B times the probability that cause Aj should occur at all, all divided by a scaling factor equal to the sum of such terms for all j possible causes. Adapting this theorem to pattern recognition in audio signals involves computing all probabilities of a given frame occurring given the set of preceding frames.
Standard generic pattern recognition algorithms are highly computationally intensive in nature due to the high data volumes required to train them and the number of probabilities that need to be computed for each test case. In addition, such algorithms do not take into account the specific characteristics of audio signals.
There is, therefore, a need in the art for computationally simple techniques of determining whether a frame from a given audio signal is similar to a test frame.